Effect of sulfur fertilization on the composition, functionality, and protein quality of navy beans (<i>Phaseolus vulgaris</i>)
Bibliographic record
Abstract
Abstract Background Soil sulfur deficiency leads to reduced crop yield and quality loss, which is especially critical for pulses as sulfur is a major component of methionine and cysteine. The present research evaluated the influence of sulfur fertilization on two varieties of navy beans grown at soil sulfur levels of 17, 26, 35, and 44 kg/ha by analyzing their composition, functionality, and protein quality. Results Increasing the soil application of sulfur did not affect the protein content of the test navy bean flours. Methionine, cysteine, and tryptophan were limiting, and their concentrations were not enhanced by sulfur fertilization nor was protein digestibility. The oil holding capacity and emulsion stability were different among sulfur treatments; however, no clear trend was observed. Conclusion Sulfur fertilization had only a minimal effect on the quality attributes of navy beans, possibly due to a combined effect of environmental factors that limited the plant's response to sulfur supply. Despite the limited change, the findings underlined the possible role of the environment (e.g., location, crop year, and other variables) in determining the sulfur fertilization response of navy beans and provided valuable insight for researchers to further investigate the topic and optimize navy bean production and quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".